In this paper, we present a new video database: CVD2014-Camera Video Database. In contrast to previous video databases, this database uses real cameras rather than introducing distortions via post-processing, which results in a complex distortion space in regard to the video acquisition process. CVD2014 contains a total of 234 videos that are recorded using 78 different cameras. Moreover, this database contains the observer-specific quality evaluation scores rather than only providing mean opinion scores. We have also collected open-ended quality descriptions that are provided by the observers. These descriptions were used to define the quality dimensions for the videos in CVD2014. The dimensions included sharpness, graininess, color balance, darkness, and jerkiness. At the end of this paper, a performance study of image and video quality algorithms for predicting the subjective video quality is reported. For this performance study, we proposed a new performance measure that accounts for observer variance. The performance study revealed that there is room for improvement regarding the video quality assessment algorithms. The CVD2014 video database has been made publicly available for the research community. All video sequences and corresponding subjective ratings can be obtained from the CVD2014 project page (http://www.helsinki.fi/psychology/groups/visualcognition/).

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This paper presents a new database, CID2013, to address the issue of using no-reference (NR) image quality assessment algorithms on images with multiple distortions. Current NR algorithms struggle to handle images with many concurrent distortion types, such as real photographic images captured by different digital cameras. The database consists of six image sets; on average, 30 subjects have evaluated 12-14 devices depicting eight different scenes for a total of 79 different cameras, 480 images, and 188 subjects (67% female). The subjective evaluation method was a hybrid absolute category rating-pair comparison developed for the study and presented in this paper. This method utilizes a slideshow of all images within a scene to allow the test images to work as references to each other. In addition to mean opinion score value, the images are also rated using sharpness, graininess, lightness, and color saturation scales. The CID2013 database contains images used in the experiments with the full subjective data plus extensive background information from the subjects. The database is made freely available for the research community.

Background:In previous years a substantial number of studies have identified statistically important predictors of nursing home admission (NHA). However, as far as we know, the analyses have been done at the population-level. No prior research has analysed the prediction accuracy of a NHA model for individuals. Methods: This study is an analysis of 3056 longer-term home care customers in the city of Tampere, Finland. Data were collected from the records of social and health service usage and RAI-HC (Resident Assessment InstrumentHome Care) assessment system during January 2011 and September 2015. The aim was to find out the most efficient variable subsets to predict NHA for individuals and validate the accuracy. The variable subsets of predicting NHA were searched by sequential forward selection (SFS) method, a variable ranking metric and the classifiers of logistic regression (LR), support vector machine (SVM) and Gaussian naive Bayes (GNB). The validation of the results was guaranteed using randomly balanced data sets and cross-validation. The primary performance metrics for the classifiers were the prediction accuracy and AUC (average area under the curve). Results: The LR and GNB classifiers achieved 78% accuracy for predicting NHA. The most important variables were RAI MAPLE (Method for Assigning Priority Levels), functional impairment (RAI IADL, Activities of Daily Living), cognitive impairment (RAI CPS, Cognitive Performance Scale), memory disorders (diagnoses G30-G32 and F00-F03) and the use of community-based health-service and prior hospital use (emergency visits and periods of care). Conclusion:The accuracy of the classifier for individuals was high enough to convince the officials of the city of Tampere to integrate the predictive model based on the findings of this study as a part of home care information system. Further work need to be done to evaluate variables that are modifiable and responsive to interventions.

Subjective image-quality estimation with high-quality images is often a preference-estimation task. Preferences are subjective, and individual differences exist. Individual differences are also seen in the eye movements of people. A task's subjectivity can result from people using different rules as a basis for their estimation. Using two studies, we investigated whether different preference-estimation rules are related to individual differences in viewing behaviour by examining the process of preference estimation of high-quality images. The estimation rules were measured from free subjective reports on important quality-related attributes (Study 1) and from estimations of the attributes’ importance in preference estimation (Study 2). The free reports showed that the observers used both feature-based image-quality attributes (e.g., sharpness, illumination) and abstract attributes, which include an interpretation of the image features (e.g., atmosphere and naturalness). In addition, the observers were classified into three viewing-strategy groups differing in fixation durations in both studies. These groups also used different estimation rules. In both studies, the group with medium-length fixations differed in their estimation rules from the other groups. In Study 1, the observers in this group used more abstract attributes than those in the other groups; in Study 2, they considered atmosphere to be a more important image feature. The study shows that individual differences in a quality-estimation task are related to both estimation rules and viewing strategies, and that the difference is related to the level of abstraction of the estimations.

We have collected a large dataset of subjective image quality "*nesses," such as sharpness or colorfulness. The dataset comes from seven studies and contains 39,415 quotations from 146 observers who have evaluated 62 scenes either in print images or on display. We analyzed the subjective evaluations and formed a hierarchical image quality attribute lexicon for *nesses, which is visualized as image quality wheel (IQ-Wheel). Similar wheel diagrams for attributes have become industry standards in other sensory experience fields such as flavor and fragrance sciences. The IQ-Wheel contains the frequency information of 68 attributes relating to image quality. Only 20% of the attributes were positive, which agrees with previous findings showing a preference for negative attributes in image quality evaluation. Our results also show that excluding physical attributes of paper gloss, observers then use similar terminology when evaluating images with printed images or images viewed on a display. IQ-Wheel can be used to guide the selection of scenes and distortions when designing subjective experimental setups and creating image databases.

Background Lean management is growing in popularity in the healthcare sector worldwide, yet healthcare organizations are struggling with assessing the maturity of their Lean implementation and monitoring its change over time. Most existing methods for such assessments are time consuming, require site visits by external consultants, and lack frontline involvement. The original Lean Healthcare Implementation Self-Assessment Instrument (LHISI) was developed by the Center for Lean Engagement and Research (CLEAR), University of California, Berkeley as a Lean principles-based survey instrument that avoids the above problems. We validated the original LHISI in the context of Finnish healthcare. Methods The original HISI survey was sent over a secure organizational email system to the over 26,000 employees of the Hospital District of Helsinki and Uusimaa in March 2020. The data were randomly split with one part used to carry out an exploratory factor analysis (EFA), and the other for testing the resulting model using confirmatory factor analysis (CFA). Results A total of 6073 employees responded to the LHISI survey, for an overall response rate of 23%. The results indicated that the 43 items used in the original LHISI can be reduced to 25 items, and these items measure a five-dimensional model of the progress of Lean implementation: leadership, commitment, standard work, communication, and daily management system. In comparison with a single-factor model, the fit measures for the 5-factor model were better: smaller X2, larger comparative fit index (CFI), smaller root mean square error of approximation (RMSEA), and smaller standardized root mean square residual (SRMR). Conclusions The 25 item LHISI is valid and feasible to use in the context of Finnish healthcare. The LHISI allows the organization to self-monitor the progress of its Lean implementation and provides the leadership with actionable knowledge to guide the path towards Lean maturity across the organization. Our findings encourage further studies on the adoption and validation of the LHISI in healthcare organizations worldwide.

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